Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics.

Fluorescence microscopy is one of the most important tools in cell biology research because it provides spatial and temporal information to investigate regulatory systems inside cells. This technique can generate data in the form of signal intensities at thousands of positions resolved inside individual live cells. However, given extensive cell-to-cell variation, these data cannot be readily assembled into three- or four-dimensional maps of protein concentration that can be compared across different cells and conditions. We have developed a method to enable comparison of imaging data from many cells and applied it to investigate actin dynamics in T cell activation. Antigen recognition in T cells by the T cell receptor (TCR) is amplified by engagement of the costimulatory receptor CD28. We imaged actin and eight core actin regulators to generate over a thousand movies of T cells under conditions in which CD28 was either engaged or blocked in the context of a strong TCR signal. Our computational analysis showed that the primary effect of costimulation blockade was to decrease recruitment of the activator of actin nucleation WAVE2 (Wiskott-Aldrich syndrome protein family verprolin-homologous protein 2) and the actin-severing protein cofilin to F-actin. Reconstitution of WAVE2 and cofilin activity restored the defect in actin signaling dynamics caused by costimulation blockade. Thus, we have developed and validated an approach to quantify protein distributions in time and space for the analysis of complex regulatory systems.

Joint modeling of cell and nuclear shape variation.

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.

Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.

We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.

Cell cycle dependence of protein subcellular location inferred from static, asynchronous images.

Protein subcellular location is one of the most important determinants of protein function during cellular processes. Changes in protein behavior during the cell cycle are expected to be involved in cellular reprogramming during disease and development, and there is therefore a critical need to understand cell-cycle dependent variation in protein localization which may be related to aberrant pathway activity. With this goal, it would be useful to have an automated method that can be applied on a proteomic scale to identify candidate proteins showing cell-cycle dependent variation of location. Fluorescence microscopy, and especially automated, high-throughput microscopy, can provide images for tens of thousands of fluorescently-tagged proteins for this purpose. Previous work on analysis of cell cycle variation has traditionally relied on obtaining time-series images over an entire cell cycle; these methods are not applicable to the single time point images that are much easier to obtain on a large scale. Hence a method that can infer cell cycle-dependence of proteins from asynchronous, static cell images would be preferable. In this work, we demonstrate such a method that can associate protein pattern variation in static images with cell cycle progression. We additionally show that a one-dimensional parameterization of cell cycle progression and protein feature pattern is sufficient to infer association between localization and cell cycle.